Matplotlib graph

import matplotlib.pyplot as plt
import numpy as np

import matplotlib as mpl

np.random.seed(19680801)  # seed the random number generator.
data = {'a': np.arange(50),
        'c': np.random.randint(0, 50, 50),
        'd': np.random.randn(50)}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100

fig, ax = plt.subplots(figsize=(5, 3.5), layout='constrained')
ax.scatter('a', 'b', c='c', s='d', data=data)
ax.set_xlabel('entry a')
ax.set_ylabel('entry b')

Text(0, 0.5, 'entry b')

Below is the Seaborn which is the only library we need to import for this simple example. A high-level API for statistical graphics

import seaborn as sns

dots = sns.load_dataset("dots")
sns.relplot(
    data=dots, kind="line",
    x="time", y="firing_rate", col="align",
    hue="choice", size="coherence", style="choice",
    facet_kws=dict(sharex=False),
)

<seaborn.axisgrid.FacetGrid at 0x1eb7ef33350>

Mapbox Density Heatmap in Python with Plotly

import plotly
plotly.offline.init_notebook_mode()

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')

import plotly.express as px
fig = px.density_mapbox(df, lat='Latitude', lon='Longitude', z='Magnitude', radius=10,
                        center=dict(lat=0, lon=180), zoom=0,
                        mapbox_style="stamen-terrain")
fig.show()

Visualize regression in scikit-learn with Plotly.

import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.neighbors import KNeighborsRegressor

df = px.data.tips()
X = df.total_bill.values.reshape(-1, 1)
x_range = np.linspace(X.min(), X.max(), 100)

# Model #1
knn_dist = KNeighborsRegressor(10, weights='distance')
knn_dist.fit(X, df.tip)
y_dist = knn_dist.predict(x_range.reshape(-1, 1))

# Model #2
knn_uni = KNeighborsRegressor(10, weights='uniform')
knn_uni.fit(X, df.tip)
y_uni = knn_uni.predict(x_range.reshape(-1, 1))

fig = px.scatter(df, x='total_bill', y='tip', color='sex', opacity=0.65)
fig.add_traces(go.Scatter(x=x_range, y=y_uni, name='Weights: Uniform'))
fig.add_traces(go.Scatter(x=x_range, y=y_dist, name='Weights: Distance'))
fig.show()